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Jeff Roach is always on the lookout for ways to improve the high-performance computations and data processing for scientific research at the University of North Carolina (UNC) at Chapel Hill. Google engineers introduced him to Techila Distributed Computing Engine (TDCE).

Wei-Tang Chang is Senior Research Associate in UNC’s Biomedical Research Imaging Center. He studies cortical layers of the brain through high-resolution functional magnetic resonance image (fMRI) datasets. Chang anticipated there would be benefits for researchers, clinicians, and patients:

“While medical imaging with high spatial resolution provides more detailed information (i.e. higher sensitivity for diagnosis), it also increases the time that the patients need to stay in the scanner. By employing an imaging acceleration technique, we can reduce the scan time but patients and doctors need to wait longer for image reconstruction. With the computational acceleration of Techila and GCP that issue could be greatly alleviated.”

The Techila Distributed Computing Engine automatically scales and distributes computing across Google Compute Engine’s high-performance virtual machine (VM) instances. It works within platforms researchers are already familiar with, like PyCharm, MATLAB and RStudio, and lets them use their own cloud accounts.

“The performance improvements gained on the proof-of-concept test case were outstanding. Depending on how busy we were, Chang used to get feedback in one to two weeks. With the Techila system and GCP we were getting feedback in three hours. That’s a huge win,” Roach reports.